plot: Plotting functions for GCMS data objects
In flagme: Analysis of Metabolomics GC/MS Data

Description Usage Arguments Details Author(s) References See Also Examples

Store the raw data and optionally, information regarding signal peaks for a number of GCMS runs

.plotpD(object, runs=1:length(object@rawdata),
       mzind=1:nrow(object@rawdata[[1]]), mind=NULL,
       plotSampleLabels=TRUE, calcGlobalMax=FALSE, peakCex = 0.8,
       plotPeaks=TRUE, plotPeakBoundaries=FALSE, plotPeakLabels=FALSE,
       plotMergedPeakLabels=TRUE, mlwd=3,usePeaks=TRUE,
       plotAcrossRuns=FALSE, overlap=F, rtrange=NULL, cols=NULL, thin=1,
       max.near=median(object@rawrt[[1]]), how.near=50, scale.up=1, ...)
			   
.plotpA(object, xlab="Peaks - run 1", ylab="Peaks - run 2",
       plotMatches=TRUE, matchPch=19, matchLwd=3,
       matchCex=.5, matchCol="black", col=colorpanel(50, "white", "green", "navyblue"),

       breaks=seq(0, 1, length=51), ...)
			   
.plotcA(object, alignment=1, ...)

`object`	a `peaksDataset`, `peaksAlignment` or `clusterAlignment` object.
`runs`	for `peaksDataset` only: set of run indices to plot
`mzind`	for `peaksDataset` only: set of mass-to-charge indices to sum over (default, all)
`mind`	for `peaksDataset` only: matrix of aligned indices
`plotSampleLabels`	for `peaksDataset` only: logical, whether to display sample labels
`calcGlobalMax`	for `peaksDataset` only: logical, whether to calculate an overall maximum for scaling
`peakCex`	character expansion factor for peak labels
`plotPeaks`	for `peaksDataset` only: logical, whether to plot hashes for each peak
`plotPeakBoundaries`	for `peaksDataset` only: logical, whether to display peak boundaries
`plotPeakLabels`	for `peaksDataset` only: logical, whether to display peak labels
`plotMergedPeakLabels`	for `peaksDataset` only: logical, whether to display 'merged' peak labels
`mlwd`	for `peaksDataset` only: line width of lines indicating the alignment
`usePeaks`	for `peaksDataset` only: logical, whether to plot alignment of peaks (otherwise, scans)
`plotAcrossRuns`	for `peaksDataset` only: logical, whether to plot across peaks when unmatched peak is given
`overlap`	for `peaksDataset` only: logical, whether to plot TIC/XICs overlapping
`rtrange`	for `peaksDataset` only: vector of length 2 giving start and end of the X-axis
`cols`	for `peaksDataset` only: vector of colours (same length as the length of runs)
`thin`	for `peaksDataset` only: when `usePeaks=FALSE`, plot the alignment lines every `thin` values
`max.near`	for `peaksDataset` only: where to look for maximum
`how.near`	for `peaksDataset` only: how far away from `max.near` to look
`scale.up`	for `peaksDataset` only: a constant factor to scale the TICs
`plotMatches`	for `peaksDataset` only: logical, whether to plot matches
`xlab`	for `peaksAlignment` and `clusterAlignment` only: x-axis label
`ylab`	for `peaksAlignment` and `clusterAlignment` only: y-axis label
`matchPch`	for `peaksAlignment` and `clusterAlignment` only: match plotting character
`matchLwd`	for `peaksAlignment` and `clusterAlignment` only: match line width
`matchCex`	for `peaksAlignment` and `clusterAlignment` only: match character expansion factor
`matchCol`	for `peaksAlignment` and `clusterAlignment` only: match colour
`col`	for `peaksAlignment` and `clusterAlignment` only: vector of colours for colourscale
`breaks`	for `peaksAlignment` and `clusterAlignment` only: vector of breaks for colourscale
`alignment`	for `peaksAlignment` and `clusterAlignment` only: the set of alignments to plot
`...`	further arguments passed to the `plot` or `image` command

For peakDataset objects, each TIC is scale to the maximum value (as specified by the how.near and max.near values). The many parameters gives considerable flexibility of how the TICs can be visualized.

For peakAlignment objects, the similarity matrix is plotted and optionally, the set of matching peaks. clusterAlignment objects are just a collection of all pairwise peakAlignment objects.

Mark Robinson

Mark D Robinson (2008). Methods for the analysis of gas chromatography - mass spectrometry data PhD dissertation University of Melbourne.

plotImage, peaksDataset

require(gcspikelite)

## paths and files
gcmsPath <- paste(find.package("gcspikelite"), "data", sep="/")
cdfFiles <- dir(gcmsPath, "CDF", full=TRUE)
eluFiles <- dir(gcmsPath, "ELU", full=TRUE)

## read data
pd <- peaksDataset(cdfFiles[1:3], mz=seq(50,550), rtrange=c(7.5,8.5))

## image plot
plot(pd, rtrange=c(7.5,8.5), plotPeaks=TRUE, plotPeakLabels=TRUE)